howardhsu/BERT-for-RRC-ABSA
Research code for BERT post-training on review reading comprehension and aspect-based sentiment analysis tasks.
Not currently ranked — collecting fresh signals.
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This repository provides code for fine-tuning and post-training BERT models on review-based NLP tasks including review reading comprehension (RRC), aspect extraction (AE), and aspect sentiment classification (ASC). It implements techniques from multiple research papers (NAACL 2019, COLING 2020, EMNLP 2020) that explore domain-specific BERT adaptation and pre-training strategies. The project includes preprocessing tools for ABSA datasets and supports cross-domain model deployment.
Frequently asked
- What is howardhsu/BERT-for-RRC-ABSA?
- Research code for BERT post-training on review reading comprehension and aspect-based sentiment analysis tasks.
- Is BERT-for-RRC-ABSA open source?
- Yes — howardhsu/BERT-for-RRC-ABSA is open source, released under the Apache-2.0 license.
- What language is BERT-for-RRC-ABSA written in?
- howardhsu/BERT-for-RRC-ABSA is primarily written in Python.
- How popular is BERT-for-RRC-ABSA?
- howardhsu/BERT-for-RRC-ABSA has 462 stars on GitHub.
- Where can I find BERT-for-RRC-ABSA?
- howardhsu/BERT-for-RRC-ABSA is on GitHub at https://github.com/howardhsu/BERT-for-RRC-ABSA.